Solutions by use case

Commercial grasping datasets differ from academic benchmarks in object diversity (500+ SKUs vs. 30-88 objects), environmental variability (mixed lighting, clutter, deformable packaging), and annotation density (6-DoF grasp poses, force profiles, failure modes). Open datasets like GraspNet-1Billion achieve 95% lab success but drop to 70% in production because they lack transparent surfaces, reflective materials, and extreme aspect ratios common in warehouses.

• Robotic grasping data
• Warehouse pick datasets

Crowdsourced RLHF relies on generalist annotators who lack robotics domain knowledge, producing preference pairs that optimize for surface-level task completion signals rather than manipulation correctness, collision avoidance, or trajectory efficiency. Expert RLHF uses roboticists, mechanical engineers, and teleoperation specialists who evaluate grasp stability, force profiles, and kinematic feasibility—producing reward models that transfer to real hardware. The Open X-Embodiment dataset aggregated 1M+ trajectories from 22 robot embodiments using expert-validated annotations[ref:ref-open-x], while crowdsourced platforms struggle to evaluate 6-DOF manipulation success criteria.

• RLHF annotation quality
• Expert preference labeling

Depth sensing training data comprises RGB-D image pairs, stereo disparity maps, LiDAR point clouds, and time-of-flight range measurements annotated for 3D scene understanding. Production robot systems require 50,000+ diverse depth samples covering transparent objects, specular surfaces, outdoor lighting, and sensor-specific noise profiles that open benchmarks like NYU Depth V2 (449 scenes) and ScanNet (1,513 scans) systematically underrepresent, driving teams toward custom collection or hybrid approaches that blend synthetic domain randomization with real-world edge-case capture.

• RGB D datasets
• Depth estimation training

The EU AI Act Article 55 requires general-purpose AI providers with systemic risk to perform adversarial testing by August 2, 2025. Truelabel supplies red-teaming datasets—physical-world edge cases, multimodal attack vectors, safety benchmarks—enabling GPAI providers to document vulnerabilities, demonstrate mitigation, and satisfy enforcement audits before the €35 million penalty threshold.

• Article 55 adversarial testing
• GPAI systemic risk compliance

Humanoid robot policies require whole-body teleoperation trajectories that capture coordinated locomotion, torso stabilization, and bimanual manipulation—data categories absent from tabletop manipulation datasets. Truelabel's marketplace connects buyers to 20,000+ verified collectors with embodiment-specific capture pipelines, delivering RLDS-formatted episodes with full kinematic chains, force-torque streams, and egocentric vision at 30–60 Hz across indoor/outdoor environments.

• Whole Body teleoperation data
• Humanoid policy training

Kitchen manipulation datasets must capture deformable food items, transparent containers, wet surfaces, and multi-step tool-use sequences that simulation cannot replicate. RoboCasa provides 2,500+ simulated object instances across 150+ layouts but lacks material properties like wet-cutting-board friction. EPIC-KITCHENS offers 100 hours of egocentric video across 45 kitchens but no robot trajectories. BridgeData V2 contains 60,000 real-robot demonstrations but only tabletop tasks in lab environments. Truelabel's marketplace connects buyers to collectors who capture custom kitchen teleoperation data with verified provenance, bridging the sim-to-real gap for household deployment.

• Kitchen robotics datasets
• Food preparation robot training

Language-conditioned robot policies require demonstrations paired with natural language instructions that describe the task being performed. Google's RT-2 model achieved 3× better generalization by training on 800,000 language-paired trajectories[ref:ref-rt2-paper], while OpenVLA outperformed the 55B-parameter RT-2-X by 16.5% using only 7B parameters trained on high-quality instruction-aligned data[ref:ref-openvla-paper]. The bottleneck is annotation quality: existing datasets suffer from template-like vocabulary, ambiguous instructions, and misaligned language-action pairs that limit policy generalization across diverse real-world tasks.

• Vision Language Action models
• Robot instruction datasets

Manipulation trajectory data pairs timestamped observation streams (RGB-D, proprioception, force-torque) with control-frequency action sequences (joint velocities, end-effector poses, gripper commands). Production policies require embodiment-matched datasets: DROID's 76,000 Franka Panda trajectories do not transfer to UR5e or Kinova arms without costly fine-tuning. Truelabel brokers custom collection campaigns that capture your exact sensor suite, action-space representation, and task distribution—eliminating the embodiment mismatch tax that degrades sim-to-real transfer by 30-50%.

• Robot manipulation datasets
• Teleoperation data collection

Multi-robot training data captures synchronized trajectories, inter-agent communication, and collision-avoidance behaviors across fleets of 2+ robots operating in shared workspaces. Unlike single-agent datasets (DROID's 76,000 solo Franka demos, BridgeData V2's isolated WidowX trajectories), multi-robot data encodes spatial coordination, task handoffs, and heterogeneous embodiment interactions required for warehouse automation, agricultural fleets, and construction teams where robots must reason about teammate positions and intentions in real time.

• Fleet coordination datasets
• Multi Agent robot learning

Open robotics datasets like Open X-Embodiment provide 1M+ trajectories across 22 embodiments at zero marginal cost, but frontier labs report that scale without task alignment produces diminishing returns. Custom collection delivers 30-78% higher task success rates by controlling embodiment, environment, and demonstration quality from intake through delivery, eliminating the data quality tax that consumes 40% of training compute in heterogeneous open datasets.

• Physical AI datasets
• Robotics training data

Red teaming data for physical AI consists of adversarial test cases designed to expose safety failures in robotics models, vision-language-action policies, and world models before deployment. Unlike automated scanners that test known vulnerability patterns, expert red teamers generate novel attack vectors—multi-step manipulation failures, sim-to-real transfer edge cases, and embodied jailbreaks—that reveal how models behave under adversarial conditions in physical environments.

• Adversarial testing data
• Physical AI safety

Safety-critical robot data captures human proximity scenarios, force-torque interactions, and edge-case failures required to train perception systems that meet ISO 10218 and ISO/TS 15066 standards. Unlike task-completion datasets, safety data prioritizes worst-case coverage: humans entering workspaces from occluded angles, partial body visibility, unusual postures, and sensor degradation conditions where detection failure causes physical harm.

• ISO 10218 robot training data
• TS 15066 compliance datasets

Sim-to-real transfer data closes the performance gap between simulated training and physical deployment by providing real-world observations that capture contact dynamics, sensor noise, and environmental variation simulators cannot reproduce. Policies trained purely in simulation suffer 30-50% task success drops on hardware; targeted real-world datasets for fine-tuning or validation reduce this gap to under 10% by exposing models to true friction coefficients, lighting conditions, and object deformations under force.

• Domain randomization
• Sim To Real gap

Vision-language-action models require synchronized triplets—visual observation, language instruction, and executed action—at each timestep. OpenVLA achieved 16.5% higher success rates than RT-2-X (55B parameters) using only 7B parameters trained on diverse demonstrations, proving data quality outweighs model scale. Truelabel's marketplace connects robotics teams to 20,000+ collectors capturing teleoperation trajectories, egocentric video with action labels, and multi-sensor demonstrations across warehouses, kitchens, and manufacturing floors in 160+ countries.

• Vision language action models
• Robot demonstration data

Warehouse robotics training data must capture SKU variety, bin clutter, and conveyor-speed constraints absent from lab datasets. Truelabel's marketplace aggregates teleoperation recordings, multi-robot collections, and custom capture services that reflect production fulfillment environments—enabling buyers to source datasets with verified provenance, embodiment metadata, and commercial licensing for pick-pack-palletize model development.

• Warehouse robot training data
• Fulfillment center datasets